Abstract
Robots playing soccer often rely on hard-coded behaviors that struggle to generalize when the game environment change. In this paper, we propose a temporal logic based approach that allows robots’ behaviors and goals to adapt to the semantics of the environment. In particular, we present a hierarchical representation of soccer in which the robot selects the level of operation based on the perceived semantic characteristics of the environment, thus modifying dynamically the set of rules and goals to apply. The proposed approach enables the robot to operate in unstructured environments, just as it happens when humans go from soccer played on an official field to soccer played on a street. Three different use cases set in different scenarios are presented to demonstrate the effectiveness of the proposed approach.
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References
Antonioni, E., Suriani, V., Riccio, F., Nardi, D.: Game strategies for physical robot soccer players: a survey. IEEE Trans. Games 13(4), 342–357 (2021)
Bastianelli, E., et al.: On-line semantic mapping. In: ICAR, pp. 1–6 (2013). https://doi.org/10.1109/ICAR.2013.6766501
Capobianco, R., Serafin, J., Dichtl, J., Grisetti, G., Iocchi, L., Nardi, D.: A proposal for semantic map representation and evaluation. In: 2015 European Conference on Mobile Robots (ECMR), pp. 1–6. IEEE (2015)
De Giacomo, G., Favorito, M., Fuggitti, F.: Planning for temporally extended goals in pure-past linear temporal logic: a polynomial reduction to standard planning (2022)
De Giacomo, G., Rubin, S.: Automata-theoretic foundations of fond planning for LTLF and LDLF goals. In: IJCAI, pp. 4729–4735 (2018)
De Giacomo, G., Vardi, M.Y.: Linear temporal logic and linear dynamic logic on finite traces. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI 2013, pp. 854–860. AAAI Press (2013)
Kurach, K., et al.: Google research football: a novel reinforcement learning environment. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 4501–4510 (2020)
Mattmüller, R., Ortlieb, M., Helmert, M., Bercher, P.: Pattern database heuristics for fully observable nondeterministic planning. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 20, no. 1, pp. 105–112 (2021). https://doi.org/10.1609/icaps.v20i1.13408
Musumeci, E., Suriani, V., Antonioni, E., Nardi, D., Bloisi, D.D.: Adaptive team behavior planning using human coach commands. In: Eguchi, A., Lau, N., Paetzel-Prüsmann, M., Wanichanon, T. (eds.) RoboCup 2022. LNCS, vol. 13561, pp. 112–123. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-28469-4_10
Pronobis, A., Jensfelt, P.: Large-scale semantic mapping and reasoning with heterogeneous modalities. In: 2012 IEEE International Conference on Robotics and Automation, pp. 3515–3522. IEEE (2012)
Röfer, T., et al.: B-Human team report and code release 2021 (2021). http://www.b-human.de/downloads/publications/2021/CodeRelease2021.pdf
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We acknowledge partial financial support from PNRR MUR project PE0000013-FAIR.
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Suriani, V., Musumeci, E., Nardi, D., Bloisi, D.D. (2024). Play Everywhere: A Temporal Logic Based Game Environment Independent Approach for Playing Soccer with Robots. In: Buche, C., Rossi, A., Simões, M., Visser, U. (eds) RoboCup 2023: Robot World Cup XXVI. RoboCup 2023. Lecture Notes in Computer Science(), vol 14140. Springer, Cham. https://doi.org/10.1007/978-3-031-55015-7_1
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DOI: https://doi.org/10.1007/978-3-031-55015-7_1
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